Task-induced decreases in cerebral blood flow are a frequent finding in functional brain imaging research but have not been adequately explained. Many of the brain areas that typically show such decreases have also been implicated in semantic processing. One hypothesis accounting for both observations is that attention-dependent, unsolicited semantic processing occurs during conscious resting state and is interrupted by shifting of attention to non-semantic tasks. Non-semantic tasks thus produce decreases in neural activity in regions normally engaged in semantic processing during rest or "thinking"). The specific aims of this proposal are to test the main predications of this model using functional magnetic resonance imaging (fMRI) and behavioral measures of semantic processing capacity. First, the model predicts that changing the attentional demands, or difficulty, or a non-semantic task should produce correlated changes in task-induced signal decreases measured with fMRI. More importantly, these changes should be correlated with reductions in semantic processing capacity measured during the same task conditions. Specific Aim 1 is to test these predictions by correlating changes in semantic processing capacity and fMRI signal decreases during controlled manipulations of attentional load in non-semantic task. Second, the model predicts that semantic tasks should not cause fMRI signal decreases, as these tasks engage the same brain areas that are engaged in semantic processing during rest. Specific Aim 2 is to test this prediction by measuring fMRI signal decreases during controlled manipulations of semantic processing load while attentional load is held constant. If correct, the main hypotheses will provide a unifying account of disparate findings from functional imaging studies of semantic processing, an explanation for at least some task induced "deactivations," and much needed information about resting state brain activity that would be broadly applicable to the design and interpretation of functional imaging experiments. These data could also provide novel research approaches to neuropsychiatric conditions characterized by abnormal thought content, including schizophrenia, obsessive compulsive disorder, affective disorder, and autism.